Layer pruning has only been scarcely addressed in literature as a special case of filter pruning [1]}. In these methods a layer is eliminated when all the values in \(\Phi \)  approaches close to zero or all the filters are turned off. Since this blocks further propagation of values across the network, it has only shown to work in case of residual networks [2]}. This makes layer pruning only a coincidental event without any explicit incentive towards loss minimization.
